Unearthing who and Y at Harewood Cemetery and inference of George Washington's Y-chromosomal haplotype.

An excavation conducted at Harewood Cemetery to identify the unmarked grave of Samuel Washington resulted in the discovery of burials presumably belonging to George Washington's paternal grandnephews and their mother, Lucy Payne. To confirm their identities this study examined Y-chromosomal, mitochondrial, and autosomal DNA from the burials and a living Washington descendant. The burial's Y-STR profile was compared to FamilyTreeDNA's database, which resulted in a one-step difference from the living descendant and an exact match to another Washington. A more complete Y-STR and Y-SNP profile from the descendant was inferred to be the Washington Y profile. Kinship comparisons performed in relation to the descendant, who is a 4th and 5th degree relative of the putative individuals, resulted in >37,000 overlapping autosomal SNPs and strong statistical support with likelihood ratios exceeding one billion. This study highlights the benefits of a multi-marker approach for kinship prediction and DNA-assisted identification of historical remains.

Extended kinship analysis of historical remains using SNP capture.

DNA-assisted identification of historical remains requires the genetic analysis of highly degraded DNA, along with a comparison to DNA from known relatives. This can be achieved by targeting single nucleotide polymorphisms (SNPs) using a hybridization capture and next-generation sequencing approach suitable for degraded skeletal samples. In the present study, two SNP capture panels were designed to target ~ 25,000 (25 K) and ~ 95,000 (95 K) nuclear SNPs, respectively, to enable distant kinship estimation (up to 4th degree relatives). Low-coverage SNP data were successfully recovered from 14 skeletal elements 75 years postmortem using an Illumina MiSeq benchtop sequencer. All samples contained degraded DNA but were of varying quality with mean fragment lengths ranging from 32 bp to 170 bp across the 14 samples. SNP comparison with DNA from known family references was performed in the Parabon Fx Forensic Analysis Platform, which utilizes a likelihood approach for kinship prediction that was optimized for low-coverage sequencing data with cytosine deamination. The 25 K panel produced 15,000 SNPs on average, which allowed for accurate kinship prediction with strong statistical support in 16 of the 21 pairwise comparisons. The 95 K panel increased the average SNPs to 42,000 and resulted in an additional accurate kinship prediction with strong statistical support (17 of 21 pairwise comparisons). This study demonstrates that SNP capture combined with massively parallel sequencing on a benchtop platform can yield sufficient SNP recovery from compromised samples, enabling accurate, extended kinship predictions.

Genetic genealogy for cold case and active investigations.

Investigative genetic genealogy has rapidly emerged as a highly effective tool for using DNA to determine the identity of unknown individuals (unidentified remains or perpetrators), generating identifications in dozens of law enforcement cases, both cold and active. The amount of press coverage of these cases may have given the impression that the analysis is straightforward and the outcome guaranteed once a sample is uploaded to a database. However, the database query results serve only as clues from which in-depth genealogy and descendancy research must proceed to determine the possible identities of an unknown individual. While there certainly will be more announcements of cases solved using this new technique, there are many more cases where identification has not yet been possible due to the wide variety of complications present in these investigations. This paper lays out the fundamentals of genetic genealogy, along with the challenges that are encountered in many of these investigations, and concludes with a set of case studies that demonstrate the variety of cases encountered thus far.

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.

BACKGROUND: Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. RESULTS: We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer's disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer's disease. CONCLUSIONS: We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.